Zeitschriftenartikel: „FUZZY-GSA“

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Shyla, S. Immaculate, und S. S. Sujatha. „An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System“. International Journal of Information Security and Privacy 14, Nr. 4 (Oktober 2020): 22–41. http://dx.doi.org/10.4018/ijisp.2020100102.

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Security incidents such as denial of service (DoS), scanning, malware code injection, viruses, worms, and password cracking are becoming common in a cloud environment that affects companies and may produce a financial loss if not detected in time. Such problems are handled by presenting an intrusion detection system (IDS) into the cloud. The existing cloud IDSs affect low detection accuracy, high false detection rate, and execution time. To overcome this problem, in this article, a gravitational search algorithm-based fuzzy inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; possibilistic fuzzy c-means (PFCM) based clustering, training based on the GSA-FIS, and testing process. Initially, the incoming data is pre-processed and clustered with the help of PFCM. PFCM detects the noise of fuzzy c-means clustering (FCM), then conquers the coincident cluster problem of possibilistic fuzzy c-means (PCM) and eradicate the row sum constraints of fuzzy possibilistic c-means clustering (FPCM). After the clustering process, the clustered data is given to the optimized fuzzy inference system (OFIS). Here, normal and abnormal data are identified by the fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely- probe, remote to local (R2L), user to root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different schemes in terms of precision, recall and F-measure.

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Chao, Chun-Tang, Ming-Tang Liu, Juing-Shian Chiou, Yi-Jung Huang und Chi-Jo Wang. „A GSA-based adaptive fuzzy PID-controller for an active suspension system“. Engineering Computations 33, Nr. 6 (01.08.2016): 1659–67. http://dx.doi.org/10.1108/ec-08-2015-0240.

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Purpose – The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA). Design/methodology/approach – The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller. Findings – The GSA can adjust the parameters of the PID-controller to achieve the optimal performance. Research limitations/implications – The GSA may have the advantage of quick convergence, but the required computation may be intensive. Practical implications – The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system. Originality/value – In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

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Bardamova, Marina, Anton Konev, Ilya Hodashinsky und Alexander Shelupanov. „A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm“. Symmetry 10, Nr. 11 (07.11.2018): 609. http://dx.doi.org/10.3390/sym10110609.

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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Yu, Xiaobing, Xianrui Yu und Xueying Zhang. „Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan“. Journal of Intelligent & Fuzzy Systems 40, Nr. 6 (21.06.2021): 11007–22. http://dx.doi.org/10.3233/jifs-202132.

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Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.

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Pramana, Setia, und Imam Habib Pamungkas. „Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm“. Jurnal Ilmu Komputer dan Informasi 11, Nr. 1 (28.02.2018): 10. http://dx.doi.org/10.21609/jiki.v11i1.580.

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Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.

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Mashfufah, Syayidati, Indah Manfaati Nur und Moh Yamin Darsyah. „Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah“. Jurnal Litbang Edusaintech 2, Nr. 1 (31.05.2021): 27–36. http://dx.doi.org/10.51402/jle.v2i1.10.

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One of the indicators of the success of social welfare development in Central Java was decreasing the population of people with social welfare problems (PMKS). One exertion that can be done was grouping or clustering the areas in Central Java-based on 26 indicators of PMKS. Fuzzy Geographically Weighted Clustering (FGWC) algorithm is a clustering analysis that observing the effect of the area. However, FGWC has a limitation in the initialization centroid phase that makes it trapped to local optimal. The limitation can be addressed with the Gravitational Search Algorithm (GSA) approach. The purpose of GSA was to optimize the value objective function. This research applied FGWC-GSA on PMKS in Central Java Province contained 26 indicators. Some validity indexes were applied to determine the best cluster. This research clustering the areas of Central Java into two clusters. The first cluster contained 24 districts and cities, and the second cluster contained 11 districts

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Hashim, H. A., und M. A. Abido. „Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study“. Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/704301.

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This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

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Kherabadi, Hossein Azadi, Sepehr Ebrahimi Mood und Mohammad Masoud Javidi. „Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller“. Cybernetics and Information Technologies 17, Nr. 1 (01.03.2017): 72–86. http://dx.doi.org/10.1515/cait-2017-0006.

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Abstract Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

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Javanbakht, Mohammad, und Mohammad Javad Mahmoodabadi. „Achieving More Stringent Levels of Comfort via an Adaptive Fuzzy Controller Optimized by the Gravitational Search Algorithm for a Half-Body Car Model“. Volume 24, No 3, September 2019 24, Nr. 3 (September 2019): 567–77. http://dx.doi.org/10.20855/jav.2019.24.31399.

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An optimal adaptive fuzzy controller is designed to achieve more stringent levels of comfort for a half-body car model. This aim will be fulfilled by reducing road disturbances and decreasing the acceleration of the body. The proposed controller consists of two adaptive fuzzy controllers with two fuzzy systems. Each one has two inputs, one output and twenty five linguistic fuzzy IF-THEN rules. Every input has five Gaussian membership functions and uses the product inference engine, singleton fuzzifier and the centre average defuzzifier. In order to determine the optimal parameters for the Adaptive Fuzzy Controller (AFC), the Gravitational Search Algorithm (GSA) is applied. The relative displacement between spring mass and tire, along with the acceleration of the body, are the two objective functions being applied in the optimization algorithm. The results illustrate the superiority of the proposed optimal adaptive fuzzy controller in comparison with traditional controllers.

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Bounar, N., S. Labdai und A. Boulkroune. „PSO–GSA based fuzzy sliding mode controller for DFIG-based wind turbine“. ISA Transactions 85 (Februar 2019): 177–88. http://dx.doi.org/10.1016/j.isatra.2018.10.020.

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Muhammad Adnan, Rana, Zhihuan Chen, Xiaohui Yuan, Ozgur Kisi, Ahmed El-Shafie, Alban Kuriqi und Misbah Ikram. „Reference Evapotranspiration Modeling Using New Heuristic Methods“. Entropy 22, Nr. 5 (13.05.2020): 547. http://dx.doi.org/10.3390/e22050547.

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The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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Hasan, Isran K., Nurwan, Nur Falaq und Muhammad Rezky Friesta Payu. „Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting“. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, Nr. 1 (02.02.2023): 120–28. http://dx.doi.org/10.29207/resti.v7i1.4508.

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Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding.

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Song, Baoye, Yihui Xiao und Lin Xu. „Design of fuzzy PI controller for brushless DC motor based on PSO–GSA algorithm“. Systems Science & Control Engineering 8, Nr. 1 (01.01.2020): 67–77. http://dx.doi.org/10.1080/21642583.2020.1723144.

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Yang, Yang, Ming Li und Xie Ma. „A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved“. Mathematical Problems in Engineering 2020 (20.10.2020): 1–13. http://dx.doi.org/10.1155/2020/5713137.

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To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

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Singh, Varsha, S. Gupta, S. Pattnaik und Aarti Goyal. „A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Controller for 1ϕ Multilevel Inverter“. International Journal of Power Electronics and Drive Systems (IJPEDS) 7, Nr. 4 (01.12.2016): 1200. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1200-1211.

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<p>This paper proposes a novel approach for obtaining a closed loop control scheme based on Fuzzy Logic Controller to regulate the output voltage waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and control the inverter to synthesize a stepped output voltage waveform with reduced harmonics. In this paper, three different intelligent soft-computing methods are used to design a fuzzy system to be used as a closed loop control system for regulating the inverter output. Gravitational Search Algorithm and Genetic Algorithm are used as optimization methods to evaluate switching angles for different combination of input voltages applied to MLI. Wavelet Transform is used as synthesizing technique to shape stepped output waveform of inverter using orthogonal wavelet sets. The proposed FLC controlled method is carried out for a wider range of input dc voltages by considering ±10% variations in nominal voltage value. A 7-level inverter is used to validate the results of proposed control methods. The three proposed methods are then compared in terms of various parameters like computational time, switching angles and THD to justify the performance and system flexibility. Finally, hardware based results are also obtained to verify the viability of the proposed method.</p>

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Mohd Basri, Mohd Ariffanan, Abdul Rashid Husain und Kumeresan A. Danapalasingam. „GSA-based optimal backstepping controller with a fuzzy compensator for robust control of an autonomous quadrotor UAV“. Aircraft Engineering and Aerospace Technology 87, Nr. 5 (07.09.2015): 493–505. http://dx.doi.org/10.1108/aeat-11-2013-0194.

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Mahmoudi, S. M., und M. Aghaie. „Evaluation of fuzzy based HS and GSA on reloading cycle length optimization of PWR nuclear power plant“. Annals of Nuclear Energy 134 (Dezember 2019): 1–10. http://dx.doi.org/10.1016/j.anucene.2019.05.058.

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Harandizadeh, Hooman, Danial Jahed Armaghani und Edy Tonnizam Mohamad. „Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets“. Neural Computing and Applications 32, Nr. 17 (27.02.2020): 14047–67. http://dx.doi.org/10.1007/s00521-020-04803-z.

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Azali, Shahin, und Mansour Sheikhan. „Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking“. Applied Intelligence 44, Nr. 1 (24.07.2015): 88–110. http://dx.doi.org/10.1007/s10489-015-0686-6.

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Nanda Kumar, S., und Nalin Kant Mohanty. „Modified Golden Jackal Optimization Assisted Adaptive Fuzzy PIDF Controller for Virtual Inertia Control of Micro Grid with Renewable Energy“. Symmetry 14, Nr. 9 (19.09.2022): 1946. http://dx.doi.org/10.3390/sym14091946.

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Frequency regulation of low inertia symmetric micro grids with the incorporation of asymmetric renewable sources such as solar and wind is a challenging task. Virtual Inertia Control (VIC) is the idea of increasing micro grids’ inertia by energy storage systems. In the current study, an adaptive fuzzy PID structure with a derivative filter (AFPIDF) controller is suggested for VIC of a micro grid with renewable sources. To optimize the proposed controllers, a modified Golden Jackal Optimization (mGJO) has been proposed, where variable Sine Cosine adopted Scaling Factor (SCaSF) is employed to adjust the Jackal’s location in the course of search process to improve the exploration and exploitation capability of the original Golden Jackal Optimization (GJO) algorithm. The performance of the mGJO algorithm is verified by equating it with original GJO, as well as Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning Based Optimization (TLBO) and Ant Lion Optimizer (ALO), considering various standard benchmark test functions. In the next stage, conventional PID and proposed FPIDF controller parameters are optimized using the proposed mGJO technique and the superiority of mGJO over other symmetric optimization algorithms is demonstrated. The robustness of the controller is also investigated under intermittent load disturbances, as well as different levels of asymmetric RESs integration.

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Kolekar, Sucheta V., Radhika M. Pai und Manohara Pai M M. „Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System“. International Journal of Emerging Technologies in Learning (iJET) 12, Nr. 06 (27.06.2017): 31. http://dx.doi.org/10.3991/ijet.v12i06.6579.

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The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.

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Sarkar, Mrinal Kanti, Subrata Banerjee, Sakti Prasad Ghoshal und Tapas Kumar Saha. „Adaptive fuzzy parameter scheduling scheme for GSA based optimal proportional integral derivative and lag-lead control of a DC attraction type levitation system“. International Journal of Automation and Control 6, Nr. 2 (2012): 174. http://dx.doi.org/10.1504/ijaac.2012.048655.

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Mohamadzadeh, Parviz, Samereh Pourmoradian, Bakhtiar Feizizadeh, Ayyoob Sharifi und Mathias Vogdrup-Schmidt. „A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran“. Sustainability 12, Nr. 24 (12.12.2020): 10413. http://dx.doi.org/10.3390/su122410413.

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We propose an efficient integrated approach of spatial decision-making systems and geographical information science for spatially explicit sustainable development mapping. The approach was developed, and its efficiency examined for sustainability assessment in East Azerbaijan Province, Iran. To achieve this goal, sustainable development indicators were employed through GIS decision rule and spatial analysis. Accordingly, 13 main criteria and 44 sub-criteria were identified and prepared as GIS dataset. The fuzzy analytical network process (FANP) method was employed to derive the criteria weights and their significance. We also applied the Global Sensitivity Analysis (GSA) for minimizing the uncertainties associated with the FANP weights. The Ordered Weighted Averaging (OWA) method was applied to aggregate the indicators and develop the sustainable development maps. Results confirmed that integrated GIS-based decision rules can be applied for any sustainable development mapping efficiently. Results of this research present an approach for sustainable development assessment and can be applied for similar research effectually. In the case of East Azerbaijan Province, the detailed results represent the unbalanced sustainable development within the different counties of this province. This requires taking necessary actions to ensure more balanced and just economic development in the province. The degree of sustainable development shows a significant spatial correlation with the industrial activities, employment, demography, poverty and infrastructure properties. The obtained results are of great importance for decision makers to identify efficient approaches in light of sustainable development mapping.

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Houssein, Essam H., Gaber M. Mohamed, Nagwan Abdel Samee, Reem Alkanhel, Ibrahim A. Ibrahim und Yaser M. Wazery. „An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation“. Diagnostics 13, Nr. 8 (15.04.2023): 1422. http://dx.doi.org/10.3390/diagnostics13081422.

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Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.

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Nadakuditi, Gouthamkumar, Harish Pulluri, Preeti Dahiya, K. S. R. Murthy, P. Srinivasa Varma, Mohit Bajaj, Torki Altameem, Walid El-Shafai und Mostafa M. Fouda. „Non-Dominated Sorting-Based Hybrid Optimization Technique for Multi-Objective Hydrothermal Scheduling“. Energies 16, Nr. 5 (28.02.2023): 2316. http://dx.doi.org/10.3390/en16052316.

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Short-term hydrothermal scheduling problem plays an important role in maintaining a high degree of economy and reliability in power system operational planning. Since electric power generation from fossil fired plants forms a major part of hydrothermal generation mix, therefore their emission contributions cannot be neglected. Hence, multi-objective short term hydrothermal scheduling is formulated as a bi-objective optimization problem by considering (a) minimizing economical power generation cost, (b) minimizing environmental emission pollution, and (c) simultaneously minimizing both the conflicting objective functions. This paper presents a non-dominated sorting disruption-based oppositional gravitational search algorithm (NSDOGSA) to solve multi-objective short-term hydrothermal scheduling (MSHTS) problems and reveals that (i) the short-term hydrothermal scheduling problem is extended to a multi-objective short-term hydrothermal scheduling problem by considering economical production cost (EPC) and environmental pollution (EEP) simultaneously while satisfying various diverse constraints; (ii) by introducing the concept of non-dominated sorting (NS) in gravitational search algorithm (GSA), it can optimize two considered objectives such as EPC and EEP simultaneously and can also obtain a group of conflicting solutions in one trial simulation; (iii) in NSDOGSA, the objective function in terms fitness for mass calculation has been represented by its rank instead of its EPC & EEP values by using the NS approach; (iv) an elite external archive set is defined to keep the NS solutions with the idea of spread indicator; (v) the optimal schedule value is extracted by using fuzzy decision approach; (vi) a consistent handling strategy has been adopted to handle effectively the system constraints; (vii) finally, the NSDOGSA approach is verified on two test systems with valve point loading effects and transmission loss, and (viii) computational discussion show that the NSDOGSA gives improved optimal results in comparison to other existing methods, which qualifies that the NSDOGSA is an effective and competitive optimization approach for solving complex MSHTS problems.

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Srinivasan N. und Lakshmi C. „Stock Price Prediction Using Fuzzy Time-Series Population Based Gravity Search Algorithm“. International Journal of Software Innovation 7, Nr. 2 (April 2019): 50–64. http://dx.doi.org/10.4018/ijsi.2019040105.

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The main motive of this research is to predict the future stock value of the particular day with minimum variation from the actual value of stock. In this research, a genetic algorithm-based gravity search algorithm is proposed for stock market prediction. It will be helpful for short-term investors in the National stock market. Some important factors that affect the value of stock are total stocks traded, total turnover of the company, gross domestic product (GDP) of the country, GDP per capita and political or external factors are some of the main factors that affect the stock value of that particular day. Opening and closing values of the stock market were predicted with the help of the above factors. Each factor will be considered as an object with mass, the mass of every object will be based on the importance. With the help of a Gravitational Search Algorithm (GSA) [1], the converging point of the entire object is determined and it is said to be the optimal output of the algorithm. The input considered are opening, closing, low and high values for a period of one year.

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„An Efficient Optimized Fuzzy Inference System based Intrusion Detection in Cloud Environment“. International Journal of Innovative Technology and Exploring Engineering 8, Nr. 12 (10.10.2019): 4285–95. http://dx.doi.org/10.35940/ijitee.l2711.1081219.

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Security incidents namely, Denial of service (DoS), scanning, virus, malware code injection, worm and password cracking are becoming common in a cloud environment that affects the company and may produce an economic loss if not detected in time. These problems are handled by presenting an intrusion detection system (IDS) in the cloud. But, the existing cloud IDSs affect from low detection accuracy, high false detection rate and execution time. To tackle these issues, in this paper, gravitational search algorithm based fuzzy Inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; Possibilistic Fuzzy C-Means (PFCM) algorithm based clustering, training based on GSA-FIS and testing process. Initially, the incoming data are pre-processed and clustered with the help of PFCM. PFCM is detecting the noise of fuzzy c-means clustering (FCM), conquer the coincident cluster problem of Possibilistic Fuzzy C-Means (PCM) and eradicate the row sum constraints of fuzzy Possibilistic c-means clustering (FPCM). After the clustering process, the clustered data are given to the optimized fuzzy Inference system (OFIS). Here, normal and abnormal data are identified by the Fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely, probe, Remote to Local (R2L), User to Root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different scheme in terms of precision, recall and F-measure

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„Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization“. International Journal of Recent Technology and Engineering 8, Nr. 5 (30.01.2020): 3206–9. http://dx.doi.org/10.35940/ijrte.d9130.018520.

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There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.

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„Adaptive Region Growing Image Segmentation Algorithms for Breast MRI“. International Journal of Recent Technology and Engineering 8, Nr. 3 (30.09.2019): 8729–32. http://dx.doi.org/10.35940/ijrte.c5912.098319.

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Early detection and characterization of breast lesion are important for a better and effective treatment of breast cancer. In this paper, four different adaptive region growing image segmentation algorithms are compared. In fact, seed selection was a vital step in the success of region growing methods, so, better schemes for seed selection methods are proposed, namely, joint probabilistic seed selection (JPSS) and Generalised simulated annealing (GSA) based seed selection. The proposed region growing methods namely Fuzzy Region Growing (FRG) and Neutrosophic Region Growing (NRG) are integrated as JPSS-FRG and GSA-NRG frameworks. Another two methods are Scale Invariant Region growing (SiRG) and Fuzzy Neutrosophic Confidence Region growing (FNCRG). The results showed that FNCRG algorithm increases breast cancer detection rate on MRI breast images with the maximum of 93% is achieved. SiRG algorithm improves the true positive rate by 13% compared to existing methods. Further, GSA-NRG makes better segmentation accuracy by 9% and true positive rate by 12%. Also, JPSS-FRG algorithm enhances segmentation accuracy by 24% and improving the true positive rate by 27% compared to Region Growing-Cellular Neural Network (RG-CNN) and Seeded Region Growing-Particle swarm optimization (SRG-PSO) methods respectively

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Das, Nitish, und Aruna Priya P. „FB-GSA: A fuzzy bi-level programming based gravitational search algorithm for unconstrained optimization“. Applied Intelligence, 17.10.2020. http://dx.doi.org/10.1007/s10489-020-01884-0.

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Shafiekhani, Sajad, Nematollah Gheibi und Azam Janati Esfahani. „Combination of anti-PD-L1 and radiotherapy in hepatocellular carcinoma: a mathematical model with uncertain parameters“. SIMULATION, 19.11.2022, 003754972211338. http://dx.doi.org/10.1177/00375497221133846.

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Blockade of programmed death-ligand-1 (PD-L1) as a new method of immunotherapy for cancers has shown limited efficacy in hepatocellular carcinoma (HCC). The combination of anti-PD-L1 and radiotherapy (RT) enhances the antitumor effect in HCC cancer. The efficacy and interactions of these treatments can be addressed by a mathematical model. We developed a mathematical model using a set of ordinary differential equations (ODEs). The variables include cancer cells, cytotoxic T lymphocytes (CTLs), programmed cell death-1 (PD-1), PD-L1, anti-PD-L1, and ionizing radiation. The model is parameterized with imprecise data set of murine HCC model and the effect of parametric uncertainty is assessed by the fuzzy theorem. The global sensitivity analysis (GSA) is performed to assess model robustness against perturbation in parameters and to identify the most influential parameters on the dynamics of cells and proteins. In silico predictions are consistent with experimental data. The model simulation shows that anti-PD-L1 and RT have a synergistic effect. In silico assessment of treatments’ efficacy in the fuzzy setting of parameters revealed that anti-PD-L1 therapy, RT, and combination treatment caused the uncertainty band of tumor cells to lead to lower populations. This model as a validated rigorous simulation framework can be used to deepen our understanding of tumor and immune cell interactions and helps clinicians to investigate the efficacy of different time schedules of anti-PD-L1, RT, and combination therapy. The fuzzy theorem in conjunction with the classical ODE model that is parameterized by imprecise data was used to predict reliable outcomes of treatment.

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Sushmitha, R., S. Chitra Devi, A. Manjula und R. Niraimathi. „A novel methodology to capture the maximum power from variable speed wind turbines using fuzzy proportional integral Controllers, modified PSO-GSA algorithm“. Materials Today: Proceedings, Februar 2021. http://dx.doi.org/10.1016/j.matpr.2020.12.905.

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„Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique“. International Journal of Innovative Technology and Exploring Engineering 8, Nr. 9 (10.07.2019): 1861–68. http://dx.doi.org/10.35940/ijitee.i8416.078919.

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Segmentation of mammogram images has gained importance in many medical treatments and diagnostic processes. Mammogram image segmentation aims at correctly separating different tissues, organs, or pathologies in volumetric image data. Most of the existing algorithms for image segmentation have a "scattered" cluster problem (disconnected clusters) happened in many clustering techniques (agglomerative, k-means, Dbscan) . Above algorithms not taken into account of both quality value and connectivity of points and region varying shapes. Two methods are proposed in this paper. The first technique is LMV (Local Map View) and the second technique is GSLMV (Gravitational Search Local Map View). LMV concentrates on determination of local quality for each point in all instances of the region in the comparative similarity view by applying the initial cluster technique. This view allows the user to choose instances for detailed analysis and filter the outlier instances from the input, next specific feature selection process identifies regions with systematic characteristics across the images. In this research work, pixels in groups with high intensity are assumed to be abnormal regions in cancer and non-cancer images. Fuzzy clustering is used to cluster the pixels. The optimal threshold from GS are initialized as cluster centres. This increases the speed of GSLMV algorithm. Performances of GSA, LMV and GSLMV methods are measured using False Rejection Rate, pixel count, Peak signal to noise ratio and runtime metrics. GSLMV showed better results based on pixel count, PSNR and runtime.

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EL-SAYED M. SAKR, Mohamed, und Mohamed A. MOUSTAFA HASSAN. „Satellite Tracking Control System Using Optimal Variable Coefficients Controllers Based on Evolutionary Optimization Techniques“. El-Cezeri Fen ve Mühendislik Dergisi, 27.04.2023. http://dx.doi.org/10.31202/ecjse.1214722.

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Satellite tracking control system is mechanism that redirects the parabolic antenna to the chosen satellite automatically. It perfectly tracks the satellite as it spins across the sky in its orbit. To maintain a continuous communication signal throughout multiple satellite tracking missions, the tracking process must be fast and smooth, with minimal deviations from the target position. Various controller models have been presented over time to address the problem of antenna positioning in satellite systems and to track moveable targets using servomechanism. The purpose of this study is to describe and debate a satellite tracking control system based on a DC servo motor. For optimal tuning of Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID) and Variable Coefficient Fractional Order PID (V-FOPID) controllers that were used in satellite control system, Particle Swarm Optimization (PSO), Gravitational Search Algorithm with Particle Swarm Optimization (GSA-PSO) and Eagle Strategy with Particle Swarm Optimization (ES-PSO) techniques were proposed. Dynamic Performance Indices Based Objective Functions is used to compute the Performance Index. Furthermore, Self-Tuning Fuzzy FOPID (STF-FOPID) is proposed for satellite tracking control system. The system's response is analyzed, and the outcomes of various control strategies are measured and compared to others. The obtained results implies that Variable Coefficient Fractional Order PID controller tuned using Eagle Strategy with Particle Swarm Optimization can precisely trace the desired position with the fastest settling time and free overshoot when compared to other control strategies.

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Dong, Jie, Lu Wang, Yanru Xing, Jun Qian, Xiao He, Jing Wu, Juan Zhou et al. „Dynamic peripheral blood microRNA expression landscape during the peri-implantation stage in women with successful pregnancy achieved by single frozen-thawed blastocyst transfer“. Human Reproduction Open, 29.08.2023. http://dx.doi.org/10.1093/hropen/hoad034.

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Abstract STUDY QUESTION What are the dynamic expression features of plasma microRNAs (miRNAs) during the peri-implantation period in women with successful pregnancy via single frozen-thawed blastocyst transfer? SUMMARY ANSWER There is a significant change in the plasma miRNA expression profile before and after blastocyst transfer, during the window of implantation. WHAT IS KNOWN ALREADY The expression of miRNAs in peripheral blood has indicative functions during the peri-implantation period. Nevertheless, the dynamic expression profile of circulating miRNAs during the peri-implantation stage in women with a successful pregnancy has not been studied. STUDY DESIGN, SIZE, DURATION Seventy-six women treated for infertility with a single frozen-thawed blastocyst transfer in a natural cycle were included in this study. Among them, 57 women had implantation success and a live birth while 19 patients experienced implantation failure. Peripheral blood samples were collected at five different time points throughout the peri-implantation period, including D0 (ovulation day), D3, D5, D7 and D9 in this cycle of embryo transfer. The plasma miRNAs in women with blastocyst transfer were isolated, sequenced and analyzed. PARTICIPANTS/MATERIALS, SETTING, METHODS Peripheral blood samples were collected in EDTA tubes and stored at -80 °C until further use. miRNAs were isolated from blood, cDNA libraries were constructed and the resulting sequences were mapped to the human genome. The plasma miRNAs were initially analyzed in a screening cohort (n = 34) with successful pregnancy. Trajectory analysis, including a global test and pairwise comparisons, was performed to detect dynamic differentially expressed (DE) miRNAs. Fuzzy C-means clustering was conducted for all dynamic DE miRNAs. The correlation between DE miRNAs and clinical characteristics of patients was investigated using a linear mixed model. Target genes of the miRNAs were predicted, and functional annotation analysis was performed. The expression of DE miRNAs was also identified in a validation set consisting of women with successful (n = 23) and unsuccessful (n = 19) pregnancies. MAIN RESULTS AND THE ROLE OF CHANCE Following small RNA sequencing, a total of 2656 miRNAs were determined as valid read values. After trajectory analysis, 26 DE miRNAs (false discovery rate (FDR) < 0.05) were identified by the global test, while pairwise comparisons in addition identified 20 DE miRNAs. A total of seven distinct clusters representing different temporal patterns of miRNA expression were discovered. Nineteen DE miRNAs were further identified to be associated with at least one clinical trait. Endometrium thickness and progesterone level showed a correlation with multiple DE miRNAs (including two of the same miRNAs, hsa-miR-1-3p and hsa-miR-6741-3p). Moreover, the 19 DE miRNAs were predicted to have 403 gene targets, and there were 51 (12.7%) predicted genes likely involved in both decidualization and embryo implantation. Functional annotation for predicted targets of those clinically related DE miRNAs suggested the involvement of vascular endothelial growth factor and Wnt signaling pathways, as well as responses to hormones, immune responses and cell adhesion-related signaling pathways during the peri-implantation stage. LARGE SCALE DATA The raw miRNA sequence data reported in this paper have been deposited in the Genome Sequence Archive (GSA-Human: HRA005227) and are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human/browse/HRA005227. LIMITATIONS, REASONS FOR CAUTION Although the RNA sequencing results revealed the global dynamic changes of miRNA expression, further experiments examining the clinical significance of the identified DE miRNAs in embryo implantation outcome and the relevant regulatory mechanisms involved are warranted. WIDER IMPLICATIONS OF THE FINDINGS Understanding the dynamic landscape of the miRNA transcriptome could shed light on the physiological mechanisms involved from ovulation to the post-implantation stage, as well as identifying biomarkers that characterize stage-related biological process. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Major clinical research project of Tangdu Hospital (2021LCYJ004) and Discipline Platform Improvement Plan of Tangdu Hospital (2020XKPT003). The funders had no influence on the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. There are no conflicts of interest to declare.

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